Evaluating the Quality of Opponent Models in Automated Bilateral Negotiations

Automated negotiation agents are agents that interact in an environment for the settlement of a mutual concern. An important factor influencing the performance of a negotiation agent is how it takes the opponent into account. The main challenge in this aspect, is that opponents typically hide private information to avoid exploitation. In such a setting, an opponent model can help by estimating the opponent's strategy or preference profile. This work contains the first recent survey of opponent models in automated negotiation. One of the main conclusions of this survey, is that currently there is no fair method to evaluate and compare the quality of a set of opponent models. Insight in the quality of an opponent model could lead to the development of a better model. In this work we focus on a specific type of opponent models which model the opponent's preferences. Based on a detailed analysis of the factors influencing the quality of this type of opponent model, we introduce and apply two fair measurement methods to quantify the performance gain relative to not using an opponent model and the accuracy of the model. Our contribution to the field of automated negotiation is threefold; first, we provide a comprehensive survey of opponent models; second, we introduce a method to isolate the components of a negotiation strategy; finally, we construct and apply two fair evaluation methods to quantify the quality of a set of opponent models which model the opponent's preferences. Taken together, this work structures the field of opponent models and provides insight in how to improve existing models.Media knowledge engineeringComputer scienceElectrical Engineering, Mathematics and Computer Scienc

IN3405 Eindverslag Bachelorproject: Applicatie voor aanvragen studie toelatingsadvies

Bachelorproject over de ALVAST applicatie voor de ULO'sMKTComputer ScienceElectrical Engineering, Mathematics and Computer Scienc

Learning about the opponent in automated bilateral negotiation: A comprehensive survey of opponent modeling techniques

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Decoupling Negotiating Agents to Explore the Space of Negotiation Strategies (extended abstract)

Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Learning about the opponent in automated bilateral negotiation: A comprehensive survey of opponent modeling techniques

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc